Combining serological assays and official statistics to describe the trajectory of the COVID-19 pandemic: results from the EPICOVID19-RS study in Rio Grande do Sul (Southern Brazil)

Hartwig, F. P.; Vidaletti, L. P.; Barros, A. J.; Victora, G. D.; Menezes, A. M.; Mesenburg, M. A.; Horta, B. L.; Silveira, M. F.; Victora, C. G.; Hallal, P. C.; Struchiner, C. J.

Background: The EPICOVID19-RS study conducted 10 population-based surveys in Rio Grande do Sul (Southern Brazil), starting early in the epidemic. The sensitivity of the rapid point-of-care test used in the first eight surveys has been shown to decrease over time after some phases of the study were concluded. The 9th survey used both the rapid test and an enzyme-linked immunosorbent assay (ELISA) test, which has a higher and stable sensitivity. Methods: We provide a theoretical justification for a correction procedure of the rapid test estimates, assess its performance in a simulated dataset and apply it to empirical data from the EPICOVID19-RS study. COVID-19 deaths from official statistics were used as an indicator of the temporal distribution of the epidemic, under the assumption that fatality is constant over time. Both the indicator and results from the 9th survey were used to calibrate the temporal decay function of the rapid test's sensitivity from a previous validation study, which was used to estimate the true sensitivity in each survey and adjust the rapid test estimates accordingly. Results: Simulations corroborated the procedure is valid. Corrected seroprevalence estimates were substantially larger than uncorrected estimates, which were substantially smaller than respective estimates from confirmed cases and therefore clearly underestimate the true infection prevalence. Conclusion: Correcting biased estimates requires a combination of data and modelling assumptions. This work illustrates the practical utility of analytical procedures, but also the critical need for good quality, populationally-representative data for tracking the progress of the epidemic and substantiate both projection models and policy making.